Skip to content
Snippets Groups Projects

Compare revisions

Changes are shown as if the source revision was being merged into the target revision. Learn more about comparing revisions.

Source

Select target project
No results found

Target

Select target project
  • ptb-843/neural_networks_101
1 result
Show changes
Commits on Source (2)
Hide whitespace changes
Inline Side-by-side
doc/image_creator.py 0 → 100644
View file @ 2a01272a
import numpy as np
import matplotlib.pyplot as plt
from scipy import interpolate
from scipy.stats import multivariate_normal
import neural_networks_101.src as src
def relu(x, slope=0):
ret = np.zeros(x.size)
idx = np.where(x >= 0)[0]
ret[idx] = x[idx]
idx = np.where(x < 0)[0]
ret[idx] = slope * x[idx]
return ret
def softmax(z):
return np.exp(z) / np.sum(np.exp(z))
def argmax(z):
ret = np.zeros(z.size)
idx = np.argmax(z)
ret[idx] = z[idx]
return ret
def maxpool(z):
ret = np.zeros((2, 2))
ret[0, 0] = np.max(z[:2, :2])
ret[0, 1] = np.max(z[:2, 2:])
ret[1, 0] = np.max(z[2:, :2])
ret[1, 1] = np.max(z[2:, 2:])
return ret
def spline_interp(x, y, res):
tck = interpolate.splrep(x, y, s=0, k=3)
x_new = np.linspace(np.min(x), np.max(x), res)
y_fit = interpolate.BSpline(*tck)(x_new)
return x_new, y_fit
def plot_relu(file_name):
x = np.linspace(-2, 2, 200)
with plt.xkcd():
fig = plt.figure()
plt.axvline(x=0, ls="--", color="k")
plt.plot(x, np.zeros(x.size), ls="--", color="k")
plt.plot(x, relu(x))
plt.savefig(file_name, dpi=200)
def plot_leaky_relu(file_name):
x = np.linspace(-2, 2, 200)
with plt.xkcd():
fig = plt.figure()
plt.axvline(x=0, ls="--", color="k")
plt.plot(x, np.zeros(x.size), ls="--", color="k")
plt.plot(x, relu(x, 0.1))
plt.savefig(file_name, dpi=200)
def plot_tanh(file_name):
x = np.linspace(-3, 3, 200)
with plt.xkcd():
fig = plt.figure()
plt.axvline(x=0, ls="--", color="k")
plt.plot(x, np.zeros(x.size), ls="--", color="k")
plt.plot(x, np.tanh(x))
plt.savefig(file_name, dpi=200)
def plot_softmax(file_name):
z = np.array([0.229, 0.070, 1.163, 1.826, 1.184,
1.311, 0.189, 0.200, 1.881, 0.738])
val = softmax(z)
with plt.xkcd():
fig = plt.figure()
plt.bar(range(1, z.size+1), val)
x, y = spline_interp(np.arange(1, z.size+1), val, 200)
plt.plot(x, y, c="k")
plt.savefig(file_name, dpi=200)
def plot_argmax(file_name):
z = np.array([0.229, 0.070, 1.163, 1.826, 1.184,
1.311, 0.189, 0.200, 1.881, 0.738])
with plt.xkcd():
fig = plt.figure()
plt.bar(range(1, z.size+1), z)
plt.bar(range(1, z.size+1), argmax(z), color="grey")
plt.savefig(file_name, dpi=200)
def plot_maxpool(file_name):
row1 = np.concatenate(
[np.random.uniform(0, 1, (2, 2)), np.random.uniform(1, 2, (2, 2))],
axis=1)
row2 = np.concatenate(
[np.random.uniform(2, 3, (2, 2)), np.random.uniform(3, 4, (2, 2))],
axis=1)
mat = np.concatenate([row1, row2], axis=0)
with plt.xkcd():
fig, ax = plt.subplot_mosaic([["big", "arrow", "small"]])
ax["big"].matshow(mat, cmap="Blues")
ax["arrow"].arrow(0.5, 1, 0.8, 0, head_width=0.2, width=0.05)
ax["arrow"].set_xlim(0, 2)
ax["arrow"].set_ylim(0, 2)
ax["arrow"].axis("off")
ax["small"].matshow(maxpool(mat), cmap="Blues")
plt.savefig(file_name, dpi=200)
def plot_venn(file_name):
with plt.xkcd():
fig, ax = plt.subplots()
ai = plt.Circle((0.5, 0.5), 0.45, color="k", fill=False)
ml = plt.Circle((0.6, 0.42), 0.3, color="k", fill=False)
nn = plt.Circle((0.5, 0.4), 0.15, color="k", fill=False)
ai_fill = plt.Circle((0.5, 0.5), 0.45, alpha=0.15)
ml_fill = plt.Circle((0.6, 0.42), 0.3, alpha=0.15)
nn_fill = plt.Circle((0.5, 0.4), 0.15, alpha=0.15)
ax.add_patch(ai_fill)
ax.add_patch(ml_fill)
ax.add_patch(nn_fill)
ax.add_patch(ai)
ax.add_patch(ml)
ax.add_patch(nn)
ax.text(0.2, 0.6, "AI", fontsize="xx-large")
ax.text(0.7, 0.5, "ML", fontsize="xx-large")
ax.text(0.4, 0.35, "NN", fontsize="xx-large")
ax.axis("off")
plt.savefig(file_name, dpi=200)
def landscape(xs):
mean1, cov1 = np.array([1, -1]), np.eye(2)
f1 = multivariate_normal(mean1, cov1)
ret = f1.pdf(xs)/f1.pdf(mean1)
mean2, cov2 = np.array([1, 1]), 0.2*np.array([[1, -.1], [.1, 1]])
f2 = multivariate_normal(mean2, cov2)
ret += f2.pdf(xs)/f2.pdf(mean2)
mean3, cov3 = np.array([-3.5, -1]), 2.0*np.array([[1, 0], [0, 5]])
f3 = multivariate_normal(mean3, cov3)
ret += f3.pdf(xs)/f3.pdf(mean3)
mean4, cov4 = np.array([0, 2]), 0.1*np.array([[1, 0], [0, 5]])
f4 = multivariate_normal(mean4, cov4)
ret += f4.pdf(xs)/f4.pdf(mean4)
mean5, cov5 = np.array([-1.5, 2]), 0.5*np.array([[3, -1], [1, 1]])
f5 = multivariate_normal(mean5, cov5)
ret += f5.pdf(xs)/f5.pdf(mean5)
return ret
def plot_sgd(file_name):
x = np.linspace(-3, 2, 50)
y = np.linspace(-1.5, 2.5, 50)
xs = src.misc.cart_prod([x, y])
fig, ax = plt.subplot_mosaic([["sgd"]])
ax["sgd"].contourf(x, x, landscape(xs).reshape(x.size, -1).T, cmap="Blues")
ax["sgd"].axis("off")
start_end = np.array([[-0.1, 1.7],
[-0.8, -1.4],
])
points1 = np.array([start_end[0],
[-0.6, 0.5],
start_end[-1],
])
points2 = np.array([start_end[0],
[-0.6, 1.6],
[-0.8, 1.7],
[-1.1, 1.4],
[-1.05, 1.2],
[-1.6, 0.7],
[-1.7, 0.4],
[-1.3, 0.0],
[-1.8, -0.3],
[-1.3, -0.8],
[-1.2, -1.4],
[-0.7, -2.4],
[-0.2, -1.6],
[-0.4, -1.0],
[-1.0, -1.2],
[-0.95, -1.5],
[-0.7, -1.6],
start_end[-1],
])
ax["sgd"].plot(points1[:, 0], points1[:, 1], "-o",
lw=4, color="k", ms=8)
ax["sgd"].plot(points1[:, 0], points1[:, 1], "-o",
lw=2, color="green")
ax["sgd"].plot(points2[:, 0], points2[:, 1], "-o",
lw=4, color="k", ms=8)
ax["sgd"].plot(points2[:, 0], points2[:, 1], "-o",
lw=2, color="orange")
ax["sgd"].plot(start_end[:, 0], start_end[:, 1], "o",
lw=4, color="k", ms=8)
ax["sgd"].plot(start_end[:, 0], start_end[:, 1], "o",
lw=2, color="w")
# color1, edgecolor1 = "orange", "k"
# ax["sgd"].arrow(x=-0.05, y=1.7, dx=-0.3, dy=-0.5, width=0.04,
# facecolor=color1, edgecolor=edgecolor1)
# ax["sgd"].arrow(x=-0.45, y=0.99, dx=-0.2, dy=-1.2, width=0.04,
# facecolor=color1, edgecolor=edgecolor1)
# color2, edgecolor2 = "green", "k"
# ax["sgd"].arrow(x=-0.05, y=1.7, dx=-0.4, dy=-0.08, width=0.04,
# facecolor=color2, edgecolor=edgecolor2)
# ax["sgd"].arrow(x=-0.05, y=1.7, dx=-0.4, dy=-0.08, width=0.04,
# facecolor=color2, edgecolor=edgecolor2)
plt.savefig(file_name, dpi=200)
def main():
"""Main."""
# plot_venn("./venn.png")
# plot_relu("./relu.png")
# plot_leaky_relu("./leaky_relu.png")
# plot_tanh("./tanh.png")
# plot_argmax("./argmax.png")
# plot_softmax("./softmax.png")
# plot_maxpool("./maxpool_tmp.png")
plot_sgd("./sgd.png")
if __name__ == "__main__":
main()
environment.yml
View file @ 2a01272a
......@@ -3,15 +3,15 @@ channels:
- pypi
- defaults
dependencies:
- autopep8=1.5.*
- jupyter=1.0.*
- pip=22.1.*
- python=3.9.*
- jupyter=1.0.*
- autopep8=1.5.*
- pip:
- ipython==8.4.*
- jupyter-contrib-nbextensions==0.5.*
- matplotlib==3.5.*
- numpy==1.22.*
- scipy==1.8.*
- torch==1.11.*
- torchvision==0.12.*
- jupyter-contrib-nbextensions==0.5.*